Perspective Chapter: Open-Source Scientific Software and Research Data in the Fourth Paradigm of the Sciences and Digital Humanities

Abstract

Data-driven sciences opened a new dimension in science and digital humanities, beginning a revolution in scientific thinking. In this context, a chapter aims to demonstrate datafication in this field. Computer software mediates data enhancement to develop scientific investigation. The readers are the scientific community in general and Library and Information Science professionals. The landscape of open-source scientific software like research data repositories, including DSpace, EPrints, Fedora, Dataverse, CKAN, dLibra, and eSciDoc, is approached. The sole purpose is to help readers understand how the complexity of datafication can serve as the basis for identifying the theoretical issues relevant to work investigation activities with scientific software and research data. The chapter is about the emergence of new fourth paradigms transforming the research world of science and digital humanities; the fourth paradigm is a concept that refers to a new way of doing science. The other three paradigms are empirical, experimental, theoretical, and simulation enhanced. Finally, it can be concluded whether information about open-source software to develop research data repositories that enable access and preservation of a wide range of research data types.

1. Introduction

Data are now stored in ever-available conditions and can be globally accessed from anywhere by any user. Digital data are a new form of information generated by all human activities with digital technologies. The data landscape manifests a strong tendency toward study and new practices that a librarian, archivists, and other Library and Information professionals in the software era. Data-Driven sciences and digital humanities are surrounded by global collaboration and the new data-information technology infrastructures used by the various branches of science and digital humanities. Scientists and social scientists’ professionals need network-computing resources to integrate, federate, and analyze data information in different locations and times [1].

In this context, the chapter is organized into the following topics. First, demonstrate a datafication of science and digital humanities research. Second, the concept of research data and scientific software; and following, present the type of the foremost open-source software for developing data repositories.

We live in a datafication of society; according to Ref. [2], communities, organizations, and all people live in a time when data is collected on anything, anytime, anywhere. A datafication of science and digital humanities research is a basis of the data-driven paradigm, a consolidation of the fourth paradigm, which we understand here as an adjective that qualifies data-oriented processes in scientific investigations. Data-driven sciences and digital humanities opened a new dimension to scientists, the data and software era universe, causing a revolution in scientific thinking. The devices, software, and hardware created through technical mediation transform our experience and raise relevant questions, creating a basis for studies in data technology. In this context, technology not only enhances our capacity to be in the world, but its impact also changes fundamental branches of the theory of knowledge, such as metaphysics, epistemology, ethics, politics, science, and other conventional ways of looking at the natural world.

In summary, the chapter is about the fourth paradigm, the emergence of new paradigms transforming the world of science and digital humanities using research data and scientific software. The fourth paradigm, a concept explained in Refs. [3, 4, 5, 6], refers to a new way of doing science. Digital and electronic science. It is the crossroads between technology and scientists. The other three paradigms are empirical, experimental, theoretical, and simulation enhanced. It is believed that digital technologies have revolutionized scientific methods. The conventional methods comprise empirical observation/exploration, theorization, and simulation [3].

In this point of view, the product of the fourth paradigm is digital research data; this digital science product is complex and fluid and includes the scientific recorded information; it is necessary to support or validate research project observations, findings, or outputs of all science and digital humanities.

Research data is collected, observed, or created in digital form for analysis to produce original research results in science and digital humanities. Does scientific evidence use a digital file, irrespective of its content or form (e.g., in print, physical objects, or other forms are digitalized), that comprises research observations, findings, or outcomes, including primary materials and analyzed data? Virtually all types of digital information have the potential to be research data if they are being used as a primary resource for scientific investigations [7, 8].

Computer science brought a new scientific paradigm that modified how scientific investigations was conducted. Technologies created new enquiry possibilities (or types). Experimental data, collected through instruments or generated by simulation, were processed by complex software systems, and only then was the resulting information (or knowledge) stored in computers.

Scientists analyzed the data only at the processing end. This context signified an essential change in the process of scientific thinking, which was replacing hypothesis formulation, experimentation, and results analysis with hypothesis formulation and answer search in the databases.

From this point of view, this chapter explains that the input of the scientific software is a digital research data; this data results from any systematic investigation involving observation, scientific experimentation, or scientific simulation. Digital research data [9] depends on the domain or scientific discipline and may differ in investigative methodologies, it must be identifiable, citable, visible, recoverable, interpretable, and reusable; thus, the requirements of consistency and precedency must be considered.

Scientific software can be understood as a set of rules or patterns of meaning and relationships, similar to political rules or scientific principles, which are systematically developed. Scientific software generates research data in digital form. Scientific software is [7, 8, 10] used by scientists during their formal education and training of new scientists to create and learn scientific technologies. Scientific software plays an essential role in decision-making, for example, making weather predictions based on climate models and computing evidence for research publications.

The other perspective in this chapter is an open-source software research data repository like scientific software. This technical and digital organizational system information helps researchers manage and store data. Also, it eases the search for and access to research data in one or multiple sources, both internal and external, in the repository. Digital repositories appeared in the early 1990s to disseminate publications and other types of digital objects. Developed with free, open-source software, they relate to openness movements. The knowledge includes Open Source Initiatives, Open Access, and Open Data. One of the first initiatives to create repositories was the arXiv developed at Cornell University in the United States, which started in 1991 as a digital library for preprints in Physics [7, 8].

This chapter discusses the importance of data-driven sciences and digital humanities. The role of information technology infrastructures in global collaboration among scientists. It explores how data-driven sciences and digital humanities trust networked computing capabilities to integrate, federate, and analyze information across different locations.

The chapter also addresses the change in traditional scientific research models by introducing digital data and scientific software, highlighting the status of research data repositories as systems for organizing and accessing scientific information records.

2. Datafication of science and a digital humanities

Digital data technologies transformed the way scientific research is conducted, leading to new scientific methodologies. This revolution is not limited to the natural sciences; the impact of digital data technologies on scientific methods can change how we approach research across all fields of study as humanities.

Data as a result of research investigations is research data in cyberspace are virtual and show characteristics of an independent world; these characteristics can be similar to or different from those found in data generated to represent the natural world. In this sense, two main components define data studies. The first is the study of the standards and norms that define the data itself. The purpose of this component is to explore the nature of data and related scientific issues without considering the meaning of the data in the natural world. The second component is the study of the rules of the natural world, as reflected by the data [11].

Data is always-on and can and will be considered as any object created digitally (digital-born) or converted to digital form (digitized), which can be used to generate insights into scientific knowledge. Data is a product of scientific investigations and an input for research. Data can be considered electronic files containing information collected systematically, structured, and documented to serve as input for further scientific studies.

Research data is scientific records information. Data is the raw research material produced through any systematic information collection for analysis [12, 13]. Research data is the same, but in hard science and digital humanity, events, and evidence can be recorded, collected, observed, and generated for scientific investigation analysis and may produce research results for a specific scientific study.

Research data must be able to be collected systematically, structured, and documented to serve as input for further research. Research data can be characterized in many ways according to its nature, origin, or status in the scientific investigation workflow. Research data may differ in its typologies. Depending on the subject or scientific discipline, the definitions may include a broad typology of digital and non-digital objects [13]. Research data proliferate because of the impulse innovations of information technologies, as technology is one of the primary data-generating sources. Thus, it is evident that research data will differ according to scientific methods and that data depend on the specific characteristics of each scientific discipline. In this sense, the next topic attempts to delimit the definition of scientific software by its usage in different scientific disciplines. The complexity of the research data concept reveals that the convolution of scientific software to understand the various dimensions between research data in sciences and digital humanities needs scientific software usage.

3. Scientific software

Scientific software can be conceptualized in multiple ways. In summary, this chapter tries a metaphor for the general concept of technology [5, 6, 7, 8, 10, 14, 15, 16, 17]. It can be seen as a technical process or methodology applied in such as computers or smartphones, which are concrete objects that require specific skills and training to use effectively. Additionally, scientific software can be understood as a set of rules or patterns of meaning and relationships, similar to political rules or scientific principles, which are systematically developed.

Computational simulation and data-intensive science revels a techno-epistemology in digital science and digital humanities and materialize scientific investigation methods software; this introduction of technologies in all methodologies change as a developed result of using digital science and digital humanities, the scientific software is part of a cyberinfrastructure, as conventional scientific models, such as theoretical and empirical models, no longer support themselves. Ins point of view, does it no longer apply? Or allow does it test theories through simulation? It emphasizes [14] the role of technology as a rule or methodology. Lastly, scientific software can be viewed as a system where hardware and software interact and are considered in the context of their users [17].

This perspective recognizes the impact of technology on extending human capacities and shaping our understanding of the world. The social construction theory suggests that technology and society are interconnected and mutually shape each other. It highlights the complex nature of technological systems and emphasizes that technology is not neutral, independent, or autonomous but a product of human actions and societal influences [14, 17, 18].

Thus, one proposal of this chapter will highlight the knowledge about scientific software used for collecting, manipulating, analyzing, and visualizing research data, called research data repositories. Thus, we assume that some of these software’s are required to preserve and curate all research. Research data repositories are used to provide research data access and preservation.

4. Research data repositories

Research data repositories are part of a cyberinfrastructure of the fourth paradigm that Library and Information professionals must master. Thus, one proposal of this chapter will highlight the knowledge and skills necessary for collecting, manipulating, analyzing, and visualizing available data in research data repositories. Hence, we assume that librarians require some of these skills.

The different kinds of technological systems created to support research data compose a rich universe of digital information that register the knowledge resulting from scientific investigation. The research data repositories may be available from two classes of providers: data providers and service providers. Data providers maintain digital document repositories and implement protocols such as the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) to make their networked metadata available. Service providers collect data for building value-added services for data, offering metadata searches or other services. The data repository should be as interoperable as possible [19, 20, 21].

The global distribution of research data repositories is cataloged by re3data, the international research data repositories registry. The re3data repository covers different academic subjects and has been registered since 2012 with approximately 3000 in international scenarios [22]. It relates data repositories for permanent storage and provides access to research datasets for funding bodies, editors, and academic institutions to promote a culture of sharing, access, and visibility for research data [23].

As said by Ref. [24], the essential function of trusted digital repositories is “[…] a mission to provide reliable, long-term access to managed digital data resources to its designated community, now and into the future.” The digital repositories must accept responsibility for the long-term maintenance of digital resources and research data, which implies having a system that supports the interoperability of digital information, provides physical responsibility and sustainability in digital media, and is designed to ensure the management, access, and security of the digital objects deposited in it. Data Repositories may be implemented with a diversity of software technologies. According to Ref. [23], the type of software applications for development repositories is CKAN (97), DSpace (126), Dataverse (167), DigitalCommons (5), EPrints (34), Fedora (48), MySQL (90) Nesstar (19), Opus (3), dLibra (3), eSciDoc (5), other (649), unknown (1187).

5. Main open-source software for the development of data repositories

A repository should establish evaluation methodologies, performance policies, and practices that can be audited and measured, such as sustainability, security, and technology infrastructure rules. Repositories should commit to reliability standards, such as those of the OAIS, which resulted in the standard ISO16363:2012, which lists the essential criteria for trusted digital repositories [19].

Data repositories may be implemented with a diversity of software technologies. However, generally, they are elaborated with free, open-source software platforms already known and internationally implemented by the library community, such as DSpace, E-prints, Fedora, Dataverse, CKAN, dLibra, and eSciDoc. This software was developed to collect, preserve, and disclose research data publications, but they can aggregate any content in a digital format with Table 1.

Software	Description
DSpace1	It was released by the MIT Libraries and Hewlett-Packard Labs in 2002 to provide a repository system for digital documents resulting from research or intended for education and distributed with an open-source license. In 2007, MIT and HP created the DSpace Foundation, a non-profit organization, to promote the platform and support its users. In 2009, this support went to the DuraSpace Foundation, a non-profit organization dedicated to open-source and cloud technologies for libraries, universities, research centres, and cultural heritage organizations.
EPrints2	The EPrints Repository Software is maintained by the School of Electronics and Computer Science, University of Southampton, UK. The platform is distributed based on an open-source license. In addition to offering the standard functionalities of institutional repositories, EPrints Services has been associated with a consulting team that can follow a project to install a repository from the analysis and customize development to provide management services.
Fedora3	Fedora is not a platform for repositories like DSpace or EPrints but an extensible architecture that can be used to develop software for repositories. Created by Cornell University, it is currently maintained by the DuraSpace Foundation. It has principles such as aggregating local content and distributed digital objects and associating these with services [Fedora]. The architecture also includes a relationship model based on the W3C’s RDF, used to bind objects to their components. It is available with an open-source license and has been used in many applications for digital libraries, archives, institutional repositories, and learning object systems.
Dataverse4	The Dataverse Network (DVN) is an open-source software used to manage data collection. The main goal of DVN is to solve data-sharing problems and replication of scientific information on the web. It supports archiving, backup, information retrieval, persistent identifiers based on fixed data patterns, metadata conversion, and preservation. The DVN facilitates the creation of the so-called dataverse. A dataverse can be a web archive or repository to store and share scientific data. The development of DVN software began in 2006 at the Institute of Quantitative Social Sciences (IQSS) at Harvard University. The concept governing the implementation of DVN is data replication; that is, a dataverse must contain the information necessary to reproduce an original study to provide an empirical analysis of the exact process of how the research data was generated or produced.
CKAN5	CKAN is a tool for creating open data repositories. It is used to manage and publish collections of data. It is widely used by research institutions and other organizations that collect data. CKAN is open-source software with an active community of developers who develop and maintain a growing library of CKAN extensions.
dLibra6	dLibra is the first Polish system for building digital libraries and has been developed by the Poznan Supercomputing And Networking Center (PSNC) since 1999. dLibra is a digital library research tool used at the PSNC since 1996. The dLibra system is now the most popular software of this type in Poland. dLibra enables the building of professional repositories of digital documents that external individuals and systems can access on the internet. Communication and data exchange is based on well-known standards and protocols, such as RSS, RDF, MARC, DublinCore, and OAI-PMH.
eSciDoc7	eSciDoc is an e-Research environment explicitly developed by scientific and scholarly communities to collaborate globally and interdisciplinarily. It comprises core functionality, including a Fedora repository (eSciDoc Infrastructure), a set of complementing services (eSciDoc Services), and an application built on top of the infrastructure and the services (eSciDoc Applications) that enables innovative e-research scenarios. Scientists, librarians, and software developers can work with research data, create novel publications, and establish new scientific and scholarly communication methods. The software is available as open-source software. The development of the eSciDoc Infrastructure ended in 2012. We do not recommend using the software for new projects due to security issues. Some former eSciDoc applications are still under active development. Please contact the Max Planck Digital Library to learn more about reuse options for the software.

Table 1.

Open-source software for the implementation of data repositories.

¹

Ref. [25].

²

Ref. [26].

³

Ref. [27].

⁴

Ref. [28].

⁵

Ref. [29].

⁶

Ref. [30].

⁷

Ref. [31].

The relevance of this software in Table 1 represents the packages because they facilitate access to, interoperability, and preservation of digital research data. The diverse ways research data flows in these systems, mainly on the internet, accentuates the need to organize, comprehend, preserve, and analyze information and knowledge that can be extracted from this digital information medium.

A research data repository is a technical information and digital organizational system to help researchers with data management and storage and ease of searching for and accessing research data in one or multiple internal and external sources in the repository. Data repositories are essential to the research cyberinfrastructure intended for preservation, long-term access, and reutilization [19].

6. Conclusions

The traditional models of scientific investigation changed with digital research data and scientific software. The fourth paradigm is a field of knowledge that also focuses on translating scientific methods to computers. As a paradigm inclusive of technology, the fourth paradigm reveals new interfaces to all scientific domains, the computer, the world wide web, and the data landscape of all digital science and digital humanities activities. The essential characteristics of the fourth paradigm are digitally-enhanced aspects, how scientists analyze, manage, gain access to, and share digital data through scientific software. Digital scientists utilize networked data and materials to formulate new information through cross-comparison and manipulation. Therefore, the fourth paradigm thrives when datasets are shared and accessible.

Data manipulation achieves paradigm exploration needs scientific software; thus, datasets used as the primary form of experimentation need scientific software. Researchers can find patterns and develop new inquiries across disciplines by manipulating and cross-comparing datasets, which requires scientific software.

The digital software in the sciences amplifies itself when mediated by computers, mainly if networks like the web are included digital research data. Currently, research data are stored in always-on conditions and can be accessed globally at any time by any user. The exponential growth of data generation is related to everything we use during our daily routine. Technological systems to support research data have created a vast universe of scientific software, the knowledge resulting from scientific investigation, and the uses of scientific software. The diverse ways research data flows in these systems, mainly on the internet, accentuates the need to organize, comprehend, preserve, and analyze information and knowledge that can be extracted from this digital information medium.

According to the context, a consolidated environment of digital research data, scientific software is the basis for developing research data repositories. Research data repositories are registered in the most diverse fields of knowledge and distributed globally. However, who is a humanities or hard sciences professional scientist in the fourth paradigm? In the software era, a Library and Information professional manages and organizes digital research data with software such as DSpace, E-prints, Fedora, Dataverse, CKAN, dLibra, and eSciDoc. With the advent of these software tools, the fourth paradigm consolidates the data share era of the cyberinfrastructure, database systems, and information management platforms to handle and provide access to ample data assets effectively.

Based on the provided about scientific software and research data, it could be further explored: the impact of data-driven sciences and digital humanities: how these technologies have transformed research practices and outcomes.

The ethical and privacy considerations in data-driven sciences: discussing informed consent, data anonymization, data security, and responsible data use to contribute to a more well-rounded exploration of data-driven sciences. The emerging trends and future directions on the current state of data-driven sciences and digital humanities are exploring emerging technologies, such as artificial intelligence, machine learning, and big data analytics, and their potential impact on research practices would provide insights into the future of data-driven sciences.